Category Archives: Data Scientist

Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. The tutorial wil give a brief understanding about Data Science.
The topics covered in the video:

Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world.
8.The Overall Map Reduce Word Count Process

Data Science is all about extracting knowledge from data. Data Science is the integration of methods from mathematics, probability models, machine learning, computer programming, statistics, data engineering, pattern recognition and learning, visualization, uncertainty modelling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products. This interdisciplinary and cross-functional field leads to decisions that move an organization forward in terms of proposed investment, decisions regarding a product or business strategy.

Data Science is a buzzword, often used interchangeably with analytics or big data. At times, Analytics is synonymous with Data Science, but at times it represents something else. A Data Scientist using raw data to build a predictive behaviour model, falls in to the category of analytics.

Data science is a steadily growing discipline that is driving significant changes across industries and in companies of every size. It is emerging as a critical source for insights for enterprises dealing with massive amounts of data.

Data Science is field of study that involves extracting meaningful insights from the data. It is a progressively growing discipline that is bringing change in the industries and companies across the world. Watch the video, which gives a detailed explanation to various concepts related to the discipline and emphasizes the Data Science in combination with the programming language R.
Following are the topics covered in the tutorial:
1.What is R?
2.Data Analysis Process
3.Why use R?
4.R: Functional Advantages
5.R Programming Concepts
6.R: Data Import Techniques
7.Processing the Data
8.Plotting Functions in R
9.Data Sub-setting: Indexing
10.Control Structures in R
11. Functions in R
Courtesy: http://www.edureka.co/data-science